Carrier’s revenue from Voice and SMS in mature wireless markets are generally in decline, while Data revenues are still steadily increasing. The main problem however, is that increases in Data revenues are not enough to negate Voice and SMS declines so, ARPU’s are relatively flat – for now. Data revenue is expected to peak in the next handful of years, thus carriers will likely seek out new revenue opportunities to offset the expected decline in data revenue. [1]

One of the opportunities sought out by carriers around the world are business models and use cases that involve the sharing of subscriber data with third parties as a means to create additional revenue streams. Data sharing models vary by use case, countries’ privacy policies, opt-in status of subscribers and a carrier’s own willingness to share data given potential backlash from subscribers.

In a 2014 industry survey conducted by Telecoms.com Intelligence, 60% of operators “believe that it is more important for Telcos to harness the power of Big Data to drive new revenue streams externally than it is to turn it to the advantage their own internal operations.” Yet when questioned further about their goals aimed at external monetization via a Big Data strategy, “the spread of responses suggested a real ambiguity in the purpose of such initiative.” Furthermore, only 10% of respondents claimed they are currently focusing on an external monetization program for their subscriber Big Data.[2] Nonetheless, several carriers in that 10% minority have already started to make progressive strides towards monetizing their data externally.

A few of the more public product announcements around the use of carrier data for external monetization include Telefonica Dynamic Insights, which, among other things, provided uses cases of anonymized mobile data delivering insights into user locations for tailored retail offerings. Many of the large U.S. mobile carriers have also developed external monetization products including Verizon Precision Market Insights, Sprint Pinsight Media, and AT&T AdWorks, which all leverage subscriber data to offer, marketers tools to create targeted mobile advertising campaigns. In addition to consumer segmentation and targeting, some other use cases for external monetization of carrier data have included credit card fraud detection at time of purchase, traffic and footfall measurement, and Real-Time Bidding modeling.

While creating supplementary revenue streams leveraging big data may appear lucrative, like any new business, the space has shown to be complicated and has its own pitfalls. Public and political pressure provide a powerful and real counter balance to the potential monetary value, particularly in the current “post Snowden” climate. For example, Telefonica Dynamic Insights launched Smart Steps in several countries in 2012. Smart Steps uses location data at an aggregate level to provide retailers with information on footfall traffic, among other things. However, backlash in Germany due to privacy concerns resulted in a quick recall of the product. In addition, AT&T pulled back support from their AdWorks mobile initiative, according to Business Insider.[3] This raises the question - is external monetization of big data a strategic investment for carriers in the near term? Additionally, will the opportunities to leverage mobile operator big data require the participation of all carriers to have enough scale to be attractive – for example, reaching 90% of the market for mobile advertising by having all the largest carriers participate, instead of only reaching <50% of the market if only one carrier has an offering?

Mobile operators have an untapped wealth of network and subscriber data that hold the answers to many key questions. External monetization of carriers’ subscriber data will grow; however, given the potential ROI risk of new use cases, changing policies, and public opinion, it is often difficult to justify the infrastructure investment necessary to prepare data for third party use. Data is typically siloed among multiple stores across departments, making it an organization-wide challenge to access the right data, particularly for a business case that may be relatively small or unproven.

Once organizational challenges are tackled, technical complexity comes into play. Carriers have multiple infrastructure vendors, either because they are products of acquisition or because they’ve made a conscious decision to have a diversified vendor base. In either case, multiple infrastructure vendors lead to complexity in normalizing data. Additionally, fusing together disparate data sets in an accurate, timely, and consistent fashion requires specialized skills. Finally, a significant infrastructure investment will likely be required to combine data sets, perform analytics and calculations, and output and store this value added data. However, given the many potential use cases from this process, many of which are still in early stages, the exact strategy of where to focus big data efforts are often still unclear.

A carrier using its data for internal monetization faces many of the same organizational, technical, and infrastructure challenges as external monetization efforts. Data siloes need to be broken down, organizational boundaries need to be crossed, data needs to be normalized across different vendors, and disparate data sets need to be linked together. But, the business case for internal monetization efforts is typically much larger than external monetization efforts. For example, providing new KPIs or data elements that decreases Average Handle Time by 2 minutes or increases First Call Resolution by 20% can translate into tens of millions of dollars annually. Because carriers have entire teams dedicated to Care, most of which have been in place for tens of years, they can more accurately estimate the benefit of additional capabilities and build credible business cases. These robust business cases can provide some of the push necessary to overcome organizational resistance to implementing an internal data analytics strategy. Another benefit of internal monetization efforts is that they are less likely to result in public or political backlash. Customers expect that carriers will use data from the network to improve their quality of service.

The main indicators, widely recognized and measured by the mobile industry to assess an operators’ overall performance, include churn rate and Average Revenue per User (ARPU). These two KPI’s expose the performance of any carrier but more importantly, provide a high-level overview of the customer experience. Business cases to improve ARPU and decrease churn require a granular understanding of network, coverage, and device performance, as well as application and service usage at the subscriber level. Unfortunately, many operators still do not have visibility into this level of detail for their data. In a recent survey conducted by Astellia, 60% of mobile operators said “they do not have access to the right data and associated tools to make truly informed business decisions.” In addition, “51% of mobile operators are not able to access crucial data related to their customer’s Quality of Experience (QoE),” for voice, messaging, data, and video usage.[4]

comScore Point of View

The opportunity to monetize big data is definitely a viable one for carriers, but will likely not have a significant contribution to the bottom line for a few years. On the other hand, internal monetization use cases require breaking down many of the same hurdles as external monetization initiatives, but are lower risk with higher ROI. Does it make sense to pursue initiatives where carriers organize data for internal use cases first and then adopt a fast follower strategy for external monetization?

To be successful, mobile operators first need to understand the implications that sharing their data streams externally may have on their own customer interactions as well as the business case for sharing data externally. Secondly, for external monetization to be effective – carriers need to ensure accuracy, security, and quality of their data. They can do so by building platforms that leverage their data internally first. But, remember this: the two are not mutually exclusive – investment in the correct internal platform does not preclude a future external monetization program.